Overview

Dataset statistics

Number of variables28
Number of observations141497
Missing cells0
Missing cells (%)0.0%
Duplicate rows10216
Duplicate rows (%)7.2%
Total size in memory30.2 MiB
Average record size in memory224.0 B

Variable types

Categorical16
Numeric12

Alerts

Dataset has 10216 (7.2%) duplicate rowsDuplicates
country has a high cardinality: 177 distinct valuesHigh cardinality
agent has a high cardinality: 334 distinct valuesHigh cardinality
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
is_canceled is highly overall correlated with reservation_statusHigh correlation
arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
children is highly imbalanced (82.0%)Imbalance
babies is highly imbalanced (97.1%)Imbalance
country is highly imbalanced (55.1%)Imbalance
distribution_channel is highly imbalanced (57.0%)Imbalance
is_repeated_guest is highly imbalanced (80.1%)Imbalance
deposit_type is highly imbalanced (64.0%)Imbalance
required_car_parking_spaces is highly imbalanced (85.5%)Imbalance
adults is highly skewed (γ1 = 25.31842147)Skewed
previous_cancellations is highly skewed (γ1 = 20.19910718)Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 25.27012254)Skewed
lead_time has 7756 (5.5%) zerosZeros
stays_in_weekend_nights has 61849 (43.7%) zerosZeros
stays_in_week_nights has 8790 (6.2%) zerosZeros
previous_cancellations has 130585 (92.3%) zerosZeros
previous_bookings_not_canceled has 137569 (97.2%) zerosZeros
booking_changes has 120590 (85.2%) zerosZeros
days_in_waiting_list has 137057 (96.9%) zerosZeros
adr has 2369 (1.7%) zerosZeros
total_of_special_requests has 85808 (60.6%) zerosZeros

Reproduction

Analysis started2023-04-09 16:51:57.653062
Analysis finished2023-04-09 16:53:44.496900
Duration1 minute and 46.84 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

hotel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
City Hotel
92673 
Resort Hotel
48824 

Length

Max length12
Median length10
Mean length10.690107
Min length10

Characters and Unicode

Total characters1512618
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel 92673
65.5%
Resort Hotel 48824
34.5%

Length

2023-04-09T13:53:44.717910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:53:45.133924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
hotel 141497
50.0%
city 92673
32.7%
resort 48824
 
17.3%

Most occurring characters

ValueCountFrequency (%)
t 282994
18.7%
o 190321
12.6%
e 190321
12.6%
141497
9.4%
H 141497
9.4%
l 141497
9.4%
C 92673
 
6.1%
i 92673
 
6.1%
y 92673
 
6.1%
R 48824
 
3.2%
Other values (2) 97648
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1088127
71.9%
Uppercase Letter 282994
 
18.7%
Space Separator 141497
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 282994
26.0%
o 190321
17.5%
e 190321
17.5%
l 141497
13.0%
i 92673
 
8.5%
y 92673
 
8.5%
s 48824
 
4.5%
r 48824
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
H 141497
50.0%
C 92673
32.7%
R 48824
 
17.3%
Space Separator
ValueCountFrequency (%)
141497
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1371121
90.6%
Common 141497
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 282994
20.6%
o 190321
13.9%
e 190321
13.9%
H 141497
10.3%
l 141497
10.3%
C 92673
 
6.8%
i 92673
 
6.8%
y 92673
 
6.8%
R 48824
 
3.6%
s 48824
 
3.6%
Common
ValueCountFrequency (%)
141497
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1512618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 282994
18.7%
o 190321
12.6%
e 190321
12.6%
141497
9.4%
H 141497
9.4%
l 141497
9.4%
C 92673
 
6.1%
i 92673
 
6.1%
y 92673
 
6.1%
R 48824
 
3.2%
Other values (2) 97648
 
6.5%

is_canceled
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
88775 
1
52722 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters141497
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 88775
62.7%
1 52722
37.3%

Length

2023-04-09T13:53:45.452383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:53:45.838713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 88775
62.7%
1 52722
37.3%

Most occurring characters

ValueCountFrequency (%)
0 88775
62.7%
1 52722
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141497
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 88775
62.7%
1 52722
37.3%

Most occurring scripts

ValueCountFrequency (%)
Common 141497
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 88775
62.7%
1 52722
37.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 88775
62.7%
1 52722
37.3%

lead_time
Real number (ℝ)

Distinct479
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.89121
Minimum0
Maximum737
Zeros7756
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:53:46.198092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median68
Q3158
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)141

Descriptive statistics

Standard deviation106.50491
Coefficient of variation (CV)1.0351216
Kurtosis1.4850697
Mean102.89121
Median Absolute Deviation (MAD)59
Skewness1.3184629
Sum14558797
Variance11343.296
MonotonicityNot monotonic
2023-04-09T13:53:46.627522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7756
 
5.5%
1 4129
 
2.9%
2 2449
 
1.7%
3 2187
 
1.5%
4 2026
 
1.4%
5 1922
 
1.4%
6 1751
 
1.2%
7 1548
 
1.1%
8 1376
 
1.0%
12 1356
 
1.0%
Other values (469) 114997
81.3%
ValueCountFrequency (%)
0 7756
5.5%
1 4129
2.9%
2 2449
 
1.7%
3 2187
 
1.5%
4 2026
 
1.4%
5 1922
 
1.4%
6 1751
 
1.2%
7 1548
 
1.1%
8 1376
 
1.0%
9 1143
 
0.8%
ValueCountFrequency (%)
737 2
 
< 0.1%
709 1
 
< 0.1%
629 17
< 0.1%
626 30
< 0.1%
622 17
< 0.1%
615 17
< 0.1%
608 17
< 0.1%
605 30
< 0.1%
601 17
< 0.1%
594 17
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2019
78989 
2020
40556 
2018
21952 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters565988
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2019 78989
55.8%
2020 40556
28.7%
2018 21952
 
15.5%

Length

2023-04-09T13:53:47.575284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:53:47.961646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2019 78989
55.8%
2020 40556
28.7%
2018 21952
 
15.5%

Most occurring characters

ValueCountFrequency (%)
2 182053
32.2%
0 182053
32.2%
1 100941
17.8%
9 78989
14.0%
8 21952
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 565988
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 182053
32.2%
0 182053
32.2%
1 100941
17.8%
9 78989
14.0%
8 21952
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 565988
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 182053
32.2%
0 182053
32.2%
1 100941
17.8%
9 78989
14.0%
8 21952
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 565988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 182053
32.2%
0 182053
32.2%
1 100941
17.8%
9 78989
14.0%
8 21952
 
3.9%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
August
17714 
October
16090 
September
15606 
July
15394 
May
11764 
Other values (7)
64929 

Length

Max length9
Median length7
Mean length6.1029562
Min length3

Characters and Unicode

Total characters863550
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August 17714
12.5%
October 16090
11.4%
September 15606
11.0%
July 15394
10.9%
May 11764
8.3%
April 11057
7.8%
June 10917
7.7%
March 9765
6.9%
December 9752
6.9%
November 9117
6.4%
Other values (2) 14321
10.1%

Length

2023-04-09T13:53:48.309299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 17714
12.5%
october 16090
11.4%
september 15606
11.0%
july 15394
10.9%
may 11764
8.3%
april 11057
7.8%
june 10917
7.7%
march 9765
6.9%
december 9752
6.9%
november 9117
6.4%
Other values (2) 14321
10.1%

Most occurring characters

ValueCountFrequency (%)
e 129652
15.0%
r 94045
 
10.9%
u 76060
 
8.8%
b 58902
 
6.8%
t 49410
 
5.7%
a 41834
 
4.8%
y 41479
 
4.8%
c 35607
 
4.1%
m 34475
 
4.0%
J 32295
 
3.7%
Other values (16) 269791
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 722053
83.6%
Uppercase Letter 141497
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 129652
18.0%
r 94045
13.0%
u 76060
10.5%
b 58902
8.2%
t 49410
 
6.8%
a 41834
 
5.8%
y 41479
 
5.7%
c 35607
 
4.9%
m 34475
 
4.8%
p 26663
 
3.7%
Other values (8) 133926
18.5%
Uppercase Letter
ValueCountFrequency (%)
J 32295
22.8%
A 28771
20.3%
M 21529
15.2%
O 16090
11.4%
S 15606
11.0%
D 9752
 
6.9%
N 9117
 
6.4%
F 8337
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 863550
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 129652
15.0%
r 94045
 
10.9%
u 76060
 
8.8%
b 58902
 
6.8%
t 49410
 
5.7%
a 41834
 
4.8%
y 41479
 
4.8%
c 35607
 
4.1%
m 34475
 
4.0%
J 32295
 
3.7%
Other values (16) 269791
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 863550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 129652
15.0%
r 94045
 
10.9%
u 76060
 
8.8%
b 58902
 
6.8%
t 49410
 
5.7%
a 41834
 
4.8%
y 41479
 
4.8%
c 35607
 
4.1%
m 34475
 
4.0%
J 32295
 
3.7%
Other values (16) 269791
31.2%

arrival_date_week_number
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.043153
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:53:48.706419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q118
median30
Q340
95-th percentile50
Maximum52
Range51
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.566284
Coefficient of variation (CV)0.46710783
Kurtosis-0.95472049
Mean29.043153
Median Absolute Deviation (MAD)11
Skewness-0.21512561
Sum4109519
Variance184.04406
MonotonicityNot monotonic
2023-04-09T13:53:49.147083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 4732
 
3.3%
52 4308
 
3.0%
41 4095
 
2.9%
38 4030
 
2.8%
42 3967
 
2.8%
32 3889
 
2.7%
39 3878
 
2.7%
30 3842
 
2.7%
34 3797
 
2.7%
29 3458
 
2.4%
Other values (42) 101501
71.7%
ValueCountFrequency (%)
1 1053
0.7%
2 1244
0.9%
3 1322
0.9%
4 1494
1.1%
5 1397
1.0%
6 1557
1.1%
7 2238
1.6%
8 2236
1.6%
9 2176
1.5%
10 2165
1.5%
ValueCountFrequency (%)
52 4308
3.0%
51 1244
 
0.9%
50 2103
1.5%
49 2619
1.9%
48 2111
1.5%
47 2361
1.7%
46 1993
1.4%
45 2488
1.8%
44 3132
2.2%
43 3283
2.3%

arrival_date_day_of_month
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.747118
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:53:49.566891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7377653
Coefficient of variation (CV)0.55488027
Kurtosis-1.1935398
Mean15.747118
Median Absolute Deviation (MAD)8
Skewness-0.0049142173
Sum2228170
Variance76.348543
MonotonicityNot monotonic
2023-04-09T13:53:49.909937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
28 8644
 
6.1%
5 5493
 
3.9%
17 5333
 
3.8%
25 5035
 
3.6%
18 4921
 
3.5%
15 4913
 
3.5%
9 4909
 
3.5%
16 4907
 
3.5%
12 4880
 
3.4%
26 4810
 
3.4%
Other values (20) 87652
61.9%
ValueCountFrequency (%)
1 4208
3.0%
2 4679
3.3%
3 4611
3.3%
4 4469
3.2%
5 5493
3.9%
6 4492
3.2%
7 4298
3.0%
8 4718
3.3%
9 4909
3.5%
10 4346
3.1%
ValueCountFrequency (%)
31 2731
 
1.9%
30 4671
3.3%
28 8644
6.1%
27 4452
3.1%
26 4810
3.4%
25 5035
3.6%
24 4714
3.3%
23 4345
3.1%
22 4163
2.9%
21 4390
3.1%

stays_in_weekend_nights
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92698078
Minimum0
Maximum19
Zeros61849
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:53:50.283682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9977981
Coefficient of variation (CV)1.0763957
Kurtosis6.5532887
Mean0.92698078
Median Absolute Deviation (MAD)1
Skewness1.3313081
Sum131165
Variance0.99560104
MonotonicityNot monotonic
2023-04-09T13:53:50.616890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 61849
43.7%
2 39644
28.0%
1 35897
25.4%
4 2202
 
1.6%
3 1506
 
1.1%
6 166
 
0.1%
5 98
 
0.1%
8 69
 
< 0.1%
7 31
 
< 0.1%
9 13
 
< 0.1%
Other values (7) 22
 
< 0.1%
ValueCountFrequency (%)
0 61849
43.7%
1 35897
25.4%
2 39644
28.0%
3 1506
 
1.1%
4 2202
 
1.6%
5 98
 
0.1%
6 166
 
0.1%
7 31
 
< 0.1%
8 69
 
< 0.1%
9 13
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
14 1
 
< 0.1%
13 3
 
< 0.1%
12 6
 
< 0.1%
10 7
 
< 0.1%
9 13
 
< 0.1%
8 69
< 0.1%
7 31
< 0.1%

stays_in_week_nights
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4944345
Minimum0
Maximum50
Zeros8790
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:53:51.031641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9003186
Coefficient of variation (CV)0.7618234
Kurtosis22.318216
Mean2.4944345
Median Absolute Deviation (MAD)1
Skewness2.7683334
Sum352955
Variance3.6112107
MonotonicityNot monotonic
2023-04-09T13:53:51.445736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 40730
28.8%
1 36341
25.7%
3 25529
18.0%
5 13087
 
9.2%
4 11155
 
7.9%
0 8790
 
6.2%
6 1848
 
1.3%
10 1244
 
0.9%
7 1243
 
0.9%
8 787
 
0.6%
Other values (23) 743
 
0.5%
ValueCountFrequency (%)
0 8790
 
6.2%
1 36341
25.7%
2 40730
28.8%
3 25529
18.0%
4 11155
 
7.9%
5 13087
 
9.2%
6 1848
 
1.3%
7 1243
 
0.9%
8 787
 
0.6%
9 274
 
0.2%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 2
 
< 0.1%
40 2
 
< 0.1%
34 1
 
< 0.1%
33 2
 
< 0.1%
32 1
 
< 0.1%
30 5
< 0.1%
26 1
 
< 0.1%
25 6
< 0.1%
24 5
< 0.1%

adults
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.858414
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:53:51.823752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum55
Range54
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62083386
Coefficient of variation (CV)0.33406651
Kurtosis1728.3002
Mean1.858414
Median Absolute Deviation (MAD)0
Skewness25.318421
Sum262960
Variance0.38543468
MonotonicityNot monotonic
2023-04-09T13:53:52.172762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 107167
75.7%
1 27564
 
19.5%
3 6664
 
4.7%
4 70
 
< 0.1%
26 10
 
< 0.1%
27 4
 
< 0.1%
20 4
 
< 0.1%
5 4
 
< 0.1%
40 2
 
< 0.1%
50 2
 
< 0.1%
Other values (3) 6
 
< 0.1%
ValueCountFrequency (%)
1 27564
 
19.5%
2 107167
75.7%
3 6664
 
4.7%
4 70
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
10 2
 
< 0.1%
20 4
 
< 0.1%
26 10
 
< 0.1%
27 4
 
< 0.1%
ValueCountFrequency (%)
55 2
 
< 0.1%
50 2
 
< 0.1%
40 2
 
< 0.1%
27 4
 
< 0.1%
26 10
 
< 0.1%
20 4
 
< 0.1%
10 2
 
< 0.1%
6 2
 
< 0.1%
5 4
 
< 0.1%
4 70
< 0.1%

children
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
132196 
1
 
5354
2
 
3876
3
 
68
10
 
3

Length

Max length2
Median length1
Mean length1.0000212
Min length1

Characters and Unicode

Total characters141500
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 132196
93.4%
1 5354
 
3.8%
2 3876
 
2.7%
3 68
 
< 0.1%
10 3
 
< 0.1%

Length

2023-04-09T13:53:52.545815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:53:52.965719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 132196
93.4%
1 5354
 
3.8%
2 3876
 
2.7%
3 68
 
< 0.1%
10 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 132199
93.4%
1 5357
 
3.8%
2 3876
 
2.7%
3 68
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 132199
93.4%
1 5357
 
3.8%
2 3876
 
2.7%
3 68
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 141500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 132199
93.4%
1 5357
 
3.8%
2 3876
 
2.7%
3 68
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 132199
93.4%
1 5357
 
3.8%
2 3876
 
2.7%
3 68
 
< 0.1%

babies
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
140368 
1
 
1109
2
 
17
9
 
2
10
 
1

Length

Max length2
Median length1
Mean length1.0000071
Min length1

Characters and Unicode

Total characters141498
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 140368
99.2%
1 1109
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%

Length

2023-04-09T13:53:53.313105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:53:53.720165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 140368
99.2%
1 1109
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 140369
99.2%
1 1110
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141498
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 140369
99.2%
1 1110
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 141498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 140369
99.2%
1 1110
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 140369
99.2%
1 1110
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%

meal
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
BB
110929 
HB
18391 
SC
 
10949
FB
 
1228

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters282994
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowHB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 110929
78.4%
HB 18391
 
13.0%
SC 10949
 
7.7%
FB 1228
 
0.9%

Length

2023-04-09T13:53:54.060699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:53:54.465233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
bb 110929
78.4%
hb 18391
 
13.0%
sc 10949
 
7.7%
fb 1228
 
0.9%

Most occurring characters

ValueCountFrequency (%)
B 241477
85.3%
H 18391
 
6.5%
S 10949
 
3.9%
C 10949
 
3.9%
F 1228
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 282994
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 241477
85.3%
H 18391
 
6.5%
S 10949
 
3.9%
C 10949
 
3.9%
F 1228
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 282994
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 241477
85.3%
H 18391
 
6.5%
S 10949
 
3.9%
C 10949
 
3.9%
F 1228
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 282994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 241477
85.3%
H 18391
 
6.5%
S 10949
 
3.9%
C 10949
 
3.9%
F 1228
 
0.4%

country
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct177
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
PRT
62822 
GBR
13461 
FRA
11746 
ESP
10493 
DEU
7796 
Other values (172)
35179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters424491
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowPRT
4th rowPRT
5th rowPRT

Common Values

ValueCountFrequency (%)
PRT 62822
44.4%
GBR 13461
 
9.5%
FRA 11746
 
8.3%
ESP 10493
 
7.4%
DEU 7796
 
5.5%
ITA 4304
 
3.0%
IRL 3855
 
2.7%
BEL 2537
 
1.8%
BRA 2361
 
1.7%
NLD 2294
 
1.6%
Other values (167) 19828
 
14.0%

Length

2023-04-09T13:53:54.800249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prt 62822
44.4%
gbr 13461
 
9.5%
fra 11746
 
8.3%
esp 10493
 
7.4%
deu 7796
 
5.5%
ita 4304
 
3.0%
irl 3855
 
2.7%
bel 2537
 
1.8%
bra 2361
 
1.7%
nld 2294
 
1.6%
Other values (167) 19828
 
14.0%

Most occurring characters

ValueCountFrequency (%)
R 98795
23.3%
P 74793
17.6%
T 69197
16.3%
A 25484
 
6.0%
E 24481
 
5.8%
B 18746
 
4.4%
S 16428
 
3.9%
G 14569
 
3.4%
U 14354
 
3.4%
F 12329
 
2.9%
Other values (16) 55315
13.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 424491
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 98795
23.3%
P 74793
17.6%
T 69197
16.3%
A 25484
 
6.0%
E 24481
 
5.8%
B 18746
 
4.4%
S 16428
 
3.9%
G 14569
 
3.4%
U 14354
 
3.4%
F 12329
 
2.9%
Other values (16) 55315
13.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 424491
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 98795
23.3%
P 74793
17.6%
T 69197
16.3%
A 25484
 
6.0%
E 24481
 
5.8%
B 18746
 
4.4%
S 16428
 
3.9%
G 14569
 
3.4%
U 14354
 
3.4%
F 12329
 
2.9%
Other values (16) 55315
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 424491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 98795
23.3%
P 74793
17.6%
T 69197
16.3%
A 25484
 
6.0%
E 24481
 
5.8%
B 18746
 
4.4%
S 16428
 
3.9%
G 14569
 
3.4%
U 14354
 
3.4%
F 12329
 
2.9%
Other values (16) 55315
13.0%

market_segment
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Online TA
62575 
Offline TA/TO
30330 
Groups
26086 
Direct
14911 
Corporate
6469 
Other values (2)
 
1126

Length

Max length13
Median length9
Mean length9.0117176
Min length6

Characters and Unicode

Total characters1275131
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnline TA
2nd rowOffline TA/TO
3rd rowOnline TA
4th rowOnline TA
5th rowDirect

Common Values

ValueCountFrequency (%)
Online TA 62575
44.2%
Offline TA/TO 30330
21.4%
Groups 26086
18.4%
Direct 14911
 
10.5%
Corporate 6469
 
4.6%
Complementary 891
 
0.6%
Aviation 235
 
0.2%

Length

2023-04-09T13:53:55.165261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:53:55.653281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
online 62575
26.7%
ta 62575
26.7%
offline 30330
12.9%
ta/to 30330
12.9%
groups 26086
11.1%
direct 14911
 
6.4%
corporate 6469
 
2.8%
complementary 891
 
0.4%
aviation 235
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 156606
12.3%
O 123235
9.7%
T 123235
9.7%
e 116067
9.1%
i 108286
8.5%
l 93796
 
7.4%
A 93140
 
7.3%
92905
 
7.3%
f 60660
 
4.8%
r 54826
 
4.3%
Other values (14) 252375
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 763929
59.9%
Uppercase Letter 387967
30.4%
Space Separator 92905
 
7.3%
Other Punctuation 30330
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 156606
20.5%
e 116067
15.2%
i 108286
14.2%
l 93796
12.3%
f 60660
 
7.9%
r 54826
 
7.2%
o 40150
 
5.3%
p 33446
 
4.4%
u 26086
 
3.4%
s 26086
 
3.4%
Other values (6) 47920
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
O 123235
31.8%
T 123235
31.8%
A 93140
24.0%
G 26086
 
6.7%
D 14911
 
3.8%
C 7360
 
1.9%
Space Separator
ValueCountFrequency (%)
92905
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 30330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1151896
90.3%
Common 123235
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 156606
13.6%
O 123235
10.7%
T 123235
10.7%
e 116067
10.1%
i 108286
9.4%
l 93796
8.1%
A 93140
8.1%
f 60660
 
5.3%
r 54826
 
4.8%
o 40150
 
3.5%
Other values (12) 181895
15.8%
Common
ValueCountFrequency (%)
92905
75.4%
/ 30330
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1275131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 156606
12.3%
O 123235
9.7%
T 123235
9.7%
e 116067
9.1%
i 108286
8.5%
l 93796
 
7.4%
A 93140
 
7.3%
92905
 
7.3%
f 60660
 
4.8%
r 54826
 
4.3%
Other values (14) 252375
19.8%

distribution_channel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
TA/TO
115716 
Direct
17452 
Corporate
 
8135
GDS
 
194

Length

Max length9
Median length5
Mean length5.3505657
Min length3

Characters and Unicode

Total characters757089
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA/TO
2nd rowTA/TO
3rd rowTA/TO
4th rowTA/TO
5th rowDirect

Common Values

ValueCountFrequency (%)
TA/TO 115716
81.8%
Direct 17452
 
12.3%
Corporate 8135
 
5.7%
GDS 194
 
0.1%

Length

2023-04-09T13:53:56.065289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:53:56.521351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 115716
81.8%
direct 17452
 
12.3%
corporate 8135
 
5.7%
gds 194
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 231432
30.6%
A 115716
15.3%
/ 115716
15.3%
O 115716
15.3%
r 33722
 
4.5%
e 25587
 
3.4%
t 25587
 
3.4%
D 17646
 
2.3%
i 17452
 
2.3%
c 17452
 
2.3%
Other values (6) 41063
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 489033
64.6%
Lowercase Letter 152340
 
20.1%
Other Punctuation 115716
 
15.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 33722
22.1%
e 25587
16.8%
t 25587
16.8%
i 17452
11.5%
c 17452
11.5%
o 16270
10.7%
p 8135
 
5.3%
a 8135
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
T 231432
47.3%
A 115716
23.7%
O 115716
23.7%
D 17646
 
3.6%
C 8135
 
1.7%
G 194
 
< 0.1%
S 194
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 115716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 641373
84.7%
Common 115716
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 231432
36.1%
A 115716
18.0%
O 115716
18.0%
r 33722
 
5.3%
e 25587
 
4.0%
t 25587
 
4.0%
D 17646
 
2.8%
i 17452
 
2.7%
c 17452
 
2.7%
o 16270
 
2.5%
Other values (5) 24793
 
3.9%
Common
ValueCountFrequency (%)
/ 115716
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 757089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 231432
30.6%
A 115716
15.3%
/ 115716
15.3%
O 115716
15.3%
r 33722
 
4.5%
e 25587
 
3.4%
t 25587
 
3.4%
D 17646
 
2.3%
i 17452
 
2.3%
c 17452
 
2.3%
Other values (6) 41063
 
5.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
137111 
1
 
4386

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters141497
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 137111
96.9%
1 4386
 
3.1%

Length

2023-04-09T13:53:56.877361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:53:57.267372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 137111
96.9%
1 4386
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 137111
96.9%
1 4386
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141497
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 137111
96.9%
1 4386
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 141497
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 137111
96.9%
1 4386
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 137111
96.9%
1 4386
 
3.1%

previous_cancellations
Real number (ℝ)

SKEWED  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12574118
Minimum0
Maximum26
Zeros130585
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:53:57.573670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0612524
Coefficient of variation (CV)8.4399748
Kurtosis447.09019
Mean0.12574118
Median Absolute Deviation (MAD)0
Skewness20.199107
Sum17792
Variance1.1262567
MonotonicityNot monotonic
2023-04-09T13:53:57.936599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 130585
92.3%
1 10307
 
7.3%
2 145
 
0.1%
24 96
 
0.1%
3 73
 
0.1%
26 52
 
< 0.1%
25 50
 
< 0.1%
19 38
 
< 0.1%
11 37
 
< 0.1%
4 31
 
< 0.1%
Other values (5) 83
 
0.1%
ValueCountFrequency (%)
0 130585
92.3%
1 10307
 
7.3%
2 145
 
0.1%
3 73
 
0.1%
4 31
 
< 0.1%
5 19
 
< 0.1%
6 22
 
< 0.1%
11 37
 
< 0.1%
13 12
 
< 0.1%
14 28
 
< 0.1%
ValueCountFrequency (%)
26 52
< 0.1%
25 50
< 0.1%
24 96
0.1%
21 2
 
< 0.1%
19 38
 
< 0.1%
14 28
 
< 0.1%
13 12
 
< 0.1%
11 37
 
< 0.1%
6 22
 
< 0.1%
5 19
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ)

SKEWED  ZEROS 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12049725
Minimum0
Maximum72
Zeros137569
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:53:58.376139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3835765
Coefficient of variation (CV)11.482224
Kurtosis890.29367
Mean0.12049725
Median Absolute Deviation (MAD)0
Skewness25.270123
Sum17050
Variance1.914284
MonotonicityNot monotonic
2023-04-09T13:53:58.826667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137569
97.2%
1 1714
 
1.2%
2 642
 
0.5%
3 361
 
0.3%
4 240
 
0.2%
5 192
 
0.1%
6 120
 
0.1%
7 94
 
0.1%
8 74
 
0.1%
9 62
 
< 0.1%
Other values (63) 429
 
0.3%
ValueCountFrequency (%)
0 137569
97.2%
1 1714
 
1.2%
2 642
 
0.5%
3 361
 
0.3%
4 240
 
0.2%
5 192
 
0.1%
6 120
 
0.1%
7 94
 
0.1%
8 74
 
0.1%
9 62
 
< 0.1%
ValueCountFrequency (%)
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%

booking_changes
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2113543
Minimum0
Maximum18
Zeros120590
Zeros (%)85.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:53:59.268683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62108295
Coefficient of variation (CV)2.9385867
Kurtosis65.243583
Mean0.2113543
Median Absolute Deviation (MAD)0
Skewness5.5266573
Sum29906
Variance0.38574404
MonotonicityNot monotonic
2023-04-09T13:53:59.608865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 120590
85.2%
1 14969
 
10.6%
2 4235
 
3.0%
3 1027
 
0.7%
4 404
 
0.3%
5 132
 
0.1%
6 62
 
< 0.1%
7 30
 
< 0.1%
8 15
 
< 0.1%
9 8
 
< 0.1%
Other values (9) 25
 
< 0.1%
ValueCountFrequency (%)
0 120590
85.2%
1 14969
 
10.6%
2 4235
 
3.0%
3 1027
 
0.7%
4 404
 
0.3%
5 132
 
0.1%
6 62
 
< 0.1%
7 30
 
< 0.1%
8 15
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 3
 
< 0.1%
16 2
 
< 0.1%
15 3
 
< 0.1%
14 3
 
< 0.1%
13 5
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 6
< 0.1%
9 8
< 0.1%

deposit_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No Deposit
122947 
Non Refund
18384 
Refundable
 
166

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1414970
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 122947
86.9%
Non Refund 18384
 
13.0%
Refundable 166
 
0.1%

Length

2023-04-09T13:54:00.046263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:54:00.451274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 122947
43.5%
deposit 122947
43.5%
non 18384
 
6.5%
refund 18384
 
6.5%
refundable 166
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 264278
18.7%
e 141663
10.0%
N 141331
10.0%
141331
10.0%
s 122947
8.7%
i 122947
8.7%
t 122947
8.7%
p 122947
8.7%
D 122947
8.7%
n 36934
 
2.6%
Other values (7) 74698
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 990811
70.0%
Uppercase Letter 282828
 
20.0%
Space Separator 141331
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 264278
26.7%
e 141663
14.3%
s 122947
12.4%
i 122947
12.4%
t 122947
12.4%
p 122947
12.4%
n 36934
 
3.7%
f 18550
 
1.9%
u 18550
 
1.9%
d 18550
 
1.9%
Other values (3) 498
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 141331
50.0%
D 122947
43.5%
R 18550
 
6.6%
Space Separator
ValueCountFrequency (%)
141331
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1273639
90.0%
Common 141331
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 264278
20.7%
e 141663
11.1%
N 141331
11.1%
s 122947
9.7%
i 122947
9.7%
t 122947
9.7%
p 122947
9.7%
D 122947
9.7%
n 36934
 
2.9%
R 18550
 
1.5%
Other values (6) 56148
 
4.4%
Common
ValueCountFrequency (%)
141331
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1414970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 264278
18.7%
e 141663
10.0%
N 141331
10.0%
141331
10.0%
s 122947
8.7%
i 122947
8.7%
t 122947
8.7%
p 122947
8.7%
D 122947
8.7%
n 36934
 
2.6%
Other values (7) 74698
 
5.3%

agent
Categorical

Distinct334
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
9
34069 
No Agent
19459 
240
16686 
1
11387 
6
 
4506
Other values (329)
55390 

Length

Max length8
Median length3
Mean length2.6862478
Min length1

Characters and Unicode

Total characters380096
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)< 0.1%

Sample

1st row240
2nd row15
3rd row240
4th row240
5th rowNo Agent

Common Values

ValueCountFrequency (%)
9 34069
24.1%
No Agent 19459
13.8%
240 16686
11.8%
1 11387
 
8.0%
6 4506
 
3.2%
14 3996
 
2.8%
7 3865
 
2.7%
250 3357
 
2.4%
241 2052
 
1.5%
28 1964
 
1.4%
Other values (324) 40156
28.4%

Length

2023-04-09T13:54:00.811817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9 34069
21.2%
no 19459
12.1%
agent 19459
12.1%
240 16686
 
10.4%
1 11387
 
7.1%
6 4506
 
2.8%
14 3996
 
2.5%
7 3865
 
2.4%
250 3357
 
2.1%
241 2052
 
1.3%
Other values (325) 42120
26.2%

Most occurring characters

ValueCountFrequency (%)
9 41339
 
10.9%
2 38859
 
10.2%
1 35086
 
9.2%
4 31908
 
8.4%
0 24506
 
6.4%
o 19459
 
5.1%
19459
 
5.1%
A 19459
 
5.1%
g 19459
 
5.1%
e 19459
 
5.1%
Other values (8) 111103
29.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 224424
59.0%
Lowercase Letter 97295
25.6%
Uppercase Letter 38918
 
10.2%
Space Separator 19459
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 41339
18.4%
2 38859
17.3%
1 35086
15.6%
4 31908
14.2%
0 24506
10.9%
3 12983
 
5.8%
7 10224
 
4.6%
5 10046
 
4.5%
6 9798
 
4.4%
8 9675
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
o 19459
20.0%
g 19459
20.0%
e 19459
20.0%
n 19459
20.0%
t 19459
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 19459
50.0%
N 19459
50.0%
Space Separator
ValueCountFrequency (%)
19459
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 243883
64.2%
Latin 136213
35.8%

Most frequent character per script

Common
ValueCountFrequency (%)
9 41339
17.0%
2 38859
15.9%
1 35086
14.4%
4 31908
13.1%
0 24506
10.0%
19459
8.0%
3 12983
 
5.3%
7 10224
 
4.2%
5 10046
 
4.1%
6 9798
 
4.0%
Latin
ValueCountFrequency (%)
o 19459
14.3%
A 19459
14.3%
g 19459
14.3%
e 19459
14.3%
n 19459
14.3%
t 19459
14.3%
N 19459
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 380096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 41339
 
10.9%
2 38859
 
10.2%
1 35086
 
9.2%
4 31908
 
8.4%
0 24506
 
6.4%
o 19459
 
5.1%
19459
 
5.1%
A 19459
 
5.1%
g 19459
 
5.1%
e 19459
 
5.1%
Other values (8) 111103
29.2%

days_in_waiting_list
Real number (ℝ)

Distinct127
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3082751
Minimum0
Maximum391
Zeros137057
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:54:01.241405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.950155
Coefficient of variation (CV)7.3432126
Kurtosis185.28278
Mean2.3082751
Median Absolute Deviation (MAD)0
Skewness11.707462
Sum326614
Variance287.30775
MonotonicityNot monotonic
2023-04-09T13:54:01.699476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137057
96.9%
58 328
 
0.2%
39 227
 
0.2%
87 160
 
0.1%
44 141
 
0.1%
31 127
 
0.1%
69 125
 
0.1%
50 110
 
0.1%
122 105
 
0.1%
77 101
 
0.1%
Other values (117) 3016
 
2.1%
ValueCountFrequency (%)
0 137057
96.9%
1 12
 
< 0.1%
2 5
 
< 0.1%
3 59
 
< 0.1%
4 25
 
< 0.1%
5 8
 
< 0.1%
6 25
 
< 0.1%
7 4
 
< 0.1%
8 7
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
391 45
< 0.1%
379 15
 
< 0.1%
330 15
 
< 0.1%
259 10
 
< 0.1%
236 35
< 0.1%
224 10
 
< 0.1%
223 61
< 0.1%
215 21
 
< 0.1%
207 15
 
< 0.1%
193 1
 
< 0.1%

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Transient
101179 
Transient-Party
32813 
Contract
 
6738
Group
 
767

Length

Max length15
Median length9
Mean length10.322092
Min length5

Characters and Unicode

Total characters1460545
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 101179
71.5%
Transient-Party 32813
 
23.2%
Contract 6738
 
4.8%
Group 767
 
0.5%

Length

2023-04-09T13:54:02.129495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:54:03.024521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
transient 101179
71.5%
transient-party 32813
 
23.2%
contract 6738
 
4.8%
group 767
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 274722
18.8%
t 180281
12.3%
r 174310
11.9%
a 173543
11.9%
T 133992
9.2%
s 133992
9.2%
i 133992
9.2%
e 133992
9.2%
y 32813
 
2.2%
- 32813
 
2.2%
Other values (7) 56095
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1253422
85.8%
Uppercase Letter 174310
 
11.9%
Dash Punctuation 32813
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 274722
21.9%
t 180281
14.4%
r 174310
13.9%
a 173543
13.8%
s 133992
10.7%
i 133992
10.7%
e 133992
10.7%
y 32813
 
2.6%
o 7505
 
0.6%
c 6738
 
0.5%
Other values (2) 1534
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
T 133992
76.9%
P 32813
 
18.8%
C 6738
 
3.9%
G 767
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 32813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1427732
97.8%
Common 32813
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 274722
19.2%
t 180281
12.6%
r 174310
12.2%
a 173543
12.2%
T 133992
9.4%
s 133992
9.4%
i 133992
9.4%
e 133992
9.4%
y 32813
 
2.3%
P 32813
 
2.3%
Other values (6) 23282
 
1.6%
Common
ValueCountFrequency (%)
- 32813
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 274722
18.8%
t 180281
12.3%
r 174310
11.9%
a 173543
11.9%
T 133992
9.2%
s 133992
9.2%
i 133992
9.2%
e 133992
9.2%
y 32813
 
2.2%
- 32813
 
2.2%
Other values (7) 56095
 
3.8%

adr
Real number (ℝ)

Distinct8857
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.549352
Minimum0
Maximum510
Zeros2369
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:54:03.428789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.8
Q166
median90.95
Q3123
95-th percentile190
Maximum510
Range510
Interquartile range (IQR)57

Descriptive statistics

Standard deviation47.542653
Coefficient of variation (CV)0.47757872
Kurtosis2.1441516
Mean99.549352
Median Absolute Deviation (MAD)27.55
Skewness1.0318292
Sum14085935
Variance2260.3038
MonotonicityNot monotonic
2023-04-09T13:54:03.934857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 6172
 
4.4%
75 3251
 
2.3%
90 3050
 
2.2%
65 2997
 
2.1%
0 2369
 
1.7%
80 2181
 
1.5%
60 1859
 
1.3%
100 1856
 
1.3%
95 1777
 
1.3%
120 1730
 
1.2%
Other values (8847) 114255
80.7%
ValueCountFrequency (%)
0 2369
1.7%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 14
 
< 0.1%
1.48 1
 
< 0.1%
1.56 2
 
< 0.1%
1.6 2
 
< 0.1%
1.8 1
 
< 0.1%
2 13
 
< 0.1%
2.4 2
 
< 0.1%
ValueCountFrequency (%)
510 1
< 0.1%
508 2
< 0.1%
451.5 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%
388 2
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
132739 
1
 
8721
2
 
32
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters141497
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 132739
93.8%
1 8721
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Length

2023-04-09T13:54:04.349876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:54:04.744411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 132739
93.8%
1 8721
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 132739
93.8%
1 8721
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141497
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 132739
93.8%
1 8721
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 141497
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 132739
93.8%
1 8721
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 132739
93.8%
1 8721
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54905051
Minimum0
Maximum5
Zeros85808
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-09T13:54:05.042422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78620746
Coefficient of variation (CV)1.4319401
Kurtosis1.5825783
Mean0.54905051
Median Absolute Deviation (MAD)0
Skewness1.3959754
Sum77689
Variance0.61812217
MonotonicityNot monotonic
2023-04-09T13:54:05.356651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 85808
60.6%
1 37411
26.4%
2 15004
 
10.6%
3 2870
 
2.0%
4 360
 
0.3%
5 44
 
< 0.1%
ValueCountFrequency (%)
0 85808
60.6%
1 37411
26.4%
2 15004
 
10.6%
3 2870
 
2.0%
4 360
 
0.3%
5 44
 
< 0.1%
ValueCountFrequency (%)
5 44
 
< 0.1%
4 360
 
0.3%
3 2870
 
2.0%
2 15004
 
10.6%
1 37411
26.4%
0 85808
60.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Check-Out
88865 
Canceled
51167 
No-Show
 
1465

Length

Max length9
Median length9
Mean length8.6176809
Min length7

Characters and Unicode

Total characters1219376
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanceled
2nd rowCanceled
3rd rowCanceled
4th rowCanceled
5th rowCanceled

Common Values

ValueCountFrequency (%)
Check-Out 88865
62.8%
Canceled 51167
36.2%
No-Show 1465
 
1.0%

Length

2023-04-09T13:54:05.708449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:54:06.127526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
check-out 88865
62.8%
canceled 51167
36.2%
no-show 1465
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 191199
15.7%
C 140032
11.5%
c 140032
11.5%
h 90330
7.4%
- 90330
7.4%
u 88865
7.3%
t 88865
7.3%
O 88865
7.3%
k 88865
7.3%
a 51167
 
4.2%
Other values (7) 160826
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 897219
73.6%
Uppercase Letter 231827
 
19.0%
Dash Punctuation 90330
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 191199
21.3%
c 140032
15.6%
h 90330
10.1%
u 88865
9.9%
t 88865
9.9%
k 88865
9.9%
a 51167
 
5.7%
n 51167
 
5.7%
l 51167
 
5.7%
d 51167
 
5.7%
Other values (2) 4395
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C 140032
60.4%
O 88865
38.3%
N 1465
 
0.6%
S 1465
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 90330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1129046
92.6%
Common 90330
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 191199
16.9%
C 140032
12.4%
c 140032
12.4%
h 90330
8.0%
u 88865
7.9%
t 88865
7.9%
O 88865
7.9%
k 88865
7.9%
a 51167
 
4.5%
n 51167
 
4.5%
Other values (6) 109659
9.7%
Common
ValueCountFrequency (%)
- 90330
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1219376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 191199
15.7%
C 140032
11.5%
c 140032
11.5%
h 90330
7.4%
- 90330
7.4%
u 88865
7.3%
t 88865
7.3%
O 88865
7.3%
k 88865
7.3%
a 51167
 
4.2%
Other values (7) 160826
13.2%

Interactions

2023-04-09T13:53:33.974867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:35.045194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:40.423133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:46.026515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:51.363526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:56.734225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:01.779410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:07.247631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:12.425303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:17.806359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:23.188555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:28.989210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:34.425001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:35.510872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:40.872076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:46.485203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:51.830165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:57.176575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:02.213783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:07.678344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:12.882276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:18.272531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:23.644975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:29.421678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:34.828418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:35.956672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:41.270281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:46.885729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:52.272949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:57.563113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:02.635794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:08.091609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:13.318357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:18.698742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:24.065925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:29.822199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:35.242057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:36.435964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:41.697667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:47.305136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:52.734780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:57.971117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:03.037898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:08.532870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:13.766608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:19.148097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:24.511508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:30.258790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:35.689738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:36.879961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:42.143074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:47.765594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:53.192904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:58.388132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:03.475461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:08.998390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:14.217294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:19.606160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:24.975754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:30.699507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:36.096999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:37.268909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:42.986811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:48.199858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:53.647949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:58.779146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:04.359940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:09.412571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:14.656083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:20.045145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:25.899462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:31.107519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:36.500248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:37.693117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:43.395460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:48.651923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:54.075479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:59.155162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:04.756743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:09.825302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:15.113419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:20.481427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:26.328755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:31.501081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:36.895101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:38.132354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:43.810512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:49.074107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:54.485513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:59.570309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:05.144379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:10.224773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:15.546051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:20.905819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:26.749549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:31.908344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:37.322482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:38.591934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:44.261820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:49.541241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:54.937836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:00.021116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:05.588453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:10.676247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:16.010599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:21.379653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:27.222399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:32.325342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:37.768794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:39.053015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:44.728477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:50.000326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:55.394498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:00.470714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:06.026546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:11.122975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:16.488988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:21.849004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:27.684112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:32.754639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:38.220353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:39.544265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:45.178531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:50.493418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:55.870757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:00.944590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:06.463590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:11.606019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:16.957441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:22.317944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:28.139844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:33.191617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:38.854999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:39.987387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:45.596611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:50.912103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:52:56.300286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:01.355541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:06.835004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:12.018816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:17.358739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:22.750787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:28.565521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-09T13:53:33.573903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-09T13:54:06.521087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
lead_timearrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultsprevious_cancellationsprevious_bookings_not_canceledbooking_changesdays_in_waiting_listadrtotal_of_special_requestshotelis_canceledarrival_date_yeararrival_date_monthchildrenbabiesmealmarket_segmentdistribution_channelis_repeated_guestdeposit_typecustomer_typerequired_car_parking_spacesreservation_status
lead_time1.0000.0430.0010.1550.2760.1940.235-0.181-0.0190.137-0.008-0.0920.1050.3080.0990.1290.0300.0100.0990.1870.1340.1210.2870.1310.0580.227
arrival_date_week_number0.0431.0000.0640.0070.002-0.0140.116-0.0390.0060.017-0.032-0.0130.0790.0710.4050.8390.0630.0180.0880.0920.0830.0660.1100.1170.0190.064
arrival_date_day_of_month0.0010.0641.000-0.016-0.0140.006-0.0200.0000.0120.0420.0340.0070.0330.0210.0370.0650.0130.0060.0480.0440.0360.0220.0630.0400.0100.025
stays_in_weekend_nights0.1550.007-0.0161.0000.2370.131-0.055-0.0800.037-0.0770.0530.0810.2020.0220.0230.0470.0310.0110.0660.0630.0640.0810.0740.0750.0120.023
stays_in_week_nights0.2760.002-0.0140.2371.0000.148-0.070-0.1150.0660.0180.1060.0910.1990.0310.0110.0360.0160.0000.0470.0380.0100.0190.0490.0710.0150.031
adults0.194-0.0140.0060.1310.1481.000-0.022-0.204-0.086-0.0320.2620.1640.0180.0170.0080.0120.0000.0000.0000.0120.0130.0000.0000.1060.0000.011
previous_cancellations0.2350.116-0.020-0.055-0.070-0.0221.0000.068-0.0900.061-0.171-0.1570.0620.0560.0340.0360.0030.0000.1160.0550.0590.1700.0640.0110.0000.040
previous_bookings_not_canceled-0.181-0.0390.000-0.080-0.115-0.2040.0681.0000.031-0.020-0.1320.0250.0160.0390.0240.0180.0000.0000.0150.0960.1140.3020.0130.0150.0160.028
booking_changes-0.0190.0060.0120.0370.066-0.086-0.0900.0311.000-0.0130.0120.0450.0500.0830.0280.0140.0270.0250.0110.0340.0510.0000.0570.0340.0270.061
days_in_waiting_list0.1370.0170.042-0.0770.018-0.0320.061-0.020-0.0131.000-0.028-0.1210.0920.0490.0650.0650.0180.0000.0340.0810.0320.0250.1050.0810.0310.038
adr-0.008-0.0320.0340.0530.1060.262-0.171-0.1320.012-0.0281.0000.2060.4100.1030.1420.1660.1990.0200.1110.1890.1490.1500.1190.1250.0500.074
total_of_special_requests-0.092-0.0130.0070.0810.0910.164-0.1570.0250.045-0.1210.2061.0000.0670.2580.0870.0530.0650.0640.0700.2210.0790.0380.2200.1180.0470.184
hotel0.1050.0790.0330.2020.1990.0180.0620.0160.0500.0920.4100.0671.0000.1370.0360.0850.0580.0500.2720.1550.1930.0340.1790.0670.2230.139
is_canceled0.3080.0710.0210.0220.0310.0170.0560.0390.0830.0490.1030.2580.1371.0000.0190.0730.0290.0350.0510.2800.1740.0620.4970.1120.1980.999
arrival_date_year0.0990.4050.0370.0230.0110.0080.0340.0240.0280.0650.1420.0870.0360.0191.0000.4030.0430.0080.1060.1420.0240.0100.0460.1670.0150.019
arrival_date_month0.1290.8390.0650.0470.0360.0120.0360.0180.0140.0650.1660.0530.0850.0730.4031.0000.0660.0190.0930.1020.0860.0680.1040.1240.0200.066
children0.0300.0630.0130.0310.0160.0000.0030.0000.0270.0180.1990.0650.0580.0290.0430.0661.0000.0250.0350.1000.0500.0310.0720.0680.0330.038
babies0.0100.0180.0060.0110.0000.0000.0000.0000.0250.0000.0200.0640.0500.0350.0080.0190.0251.0000.0170.0360.0340.0090.0240.0170.0210.024
meal0.0990.0880.0480.0660.0470.0000.1160.0150.0110.0340.1110.0700.2720.0510.1060.0930.0350.0171.0000.2160.0850.0630.0940.1270.0270.042
market_segment0.1870.0920.0440.0630.0380.0120.0550.0960.0340.0810.1890.2210.1550.2800.1420.1020.1000.0360.2161.0000.7120.3190.3640.2850.0940.206
distribution_channel0.1340.0830.0360.0640.0100.0130.0590.1140.0510.0320.1490.0790.1930.1740.0240.0860.0500.0340.0850.7121.0000.2700.0900.0890.0860.129
is_repeated_guest0.1210.0660.0220.0810.0190.0000.1700.3020.0000.0250.1500.0380.0340.0620.0100.0680.0310.0090.0630.3190.2701.0000.0540.0880.0650.063
deposit_type0.2870.1100.0630.0740.0490.0000.0640.0130.0570.1050.1190.2200.1790.4970.0460.1040.0720.0240.0940.3640.0900.0541.0000.0860.0730.357
customer_type0.1310.1170.0400.0750.0710.1060.0110.0150.0340.0810.1250.1180.0670.1120.1670.1240.0680.0170.1270.2850.0890.0880.0861.0000.0490.079
required_car_parking_spaces0.0580.0190.0100.0120.0150.0000.0000.0160.0270.0310.0500.0470.2230.1980.0150.0200.0330.0210.0270.0940.0860.0650.0730.0491.0000.140
reservation_status0.2270.0640.0250.0230.0310.0110.0400.0280.0610.0380.0740.1840.1390.9990.0190.0660.0380.0240.0420.2060.1290.0630.3570.0790.1401.000

Missing values

2023-04-09T13:53:40.018127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-09T13:53:42.359878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledbooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_status
0Resort Hotel1852018July27103200BBPRTOnline TATA/TO0000No Deposit2400Transient82.0001Canceled
1Resort Hotel1752018July27103200HBPRTOffline TA/TOTA/TO0000No Deposit150Transient105.5000Canceled
2Resort Hotel1232018July27104200BBPRTOnline TATA/TO0000No Deposit2400Transient123.0000Canceled
3Resort Hotel1602018July27125200BBPRTOnline TATA/TO0000No Deposit2400Transient107.0002Canceled
4Resort Hotel1962018July27128200BBPRTDirectDirect0000No DepositNo Agent0Transient108.3002Canceled
5Resort Hotel1452018July27213300BBPRTOnline TATA/TO0000No Deposit2410Transient108.8001Canceled
6Resort Hotel1402018July27213300BBPRTOnline TATA/TO0000No Deposit2410Transient108.8001Canceled
7Resort Hotel1432018July27213300BBPRTOnline TATA/TO0000No Deposit2410Transient108.8000Canceled
8Resort Hotel1452018July27223200BBPRTOnline TATA/TO0000No Deposit2410Transient117.8100Canceled
9Resort Hotel1472018July27225220BBPRTOnline TATA/TO0000No Deposit2400Transient153.0000Canceled
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledbooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_status
141487City Hotel12192020August352813210BBROUOnline TATA/TO0001No Deposit90Transient135.0002No-Show
141488City Hotel13202020August352825200BBDEUOnline TATA/TO0000No Deposit90Transient112.7603No-Show
141489City Hotel112020March10702200SCPRTDirectDirect1010No DepositNo Agent0Transient98.0000No-Show
141490City Hotel142020August352810100BBPRTComplementaryCorporate1030No DepositNo Agent0Transient0.0002No-Show
141491City Hotel122020June241121100BBPRTAviationCorporate1020No DepositNo Agent0Transient95.0000No-Show
141492City Hotel112020February5100100BBAUTAviationCorporate1010No DepositNo Agent0Transient0.0001No-Show
141493City Hotel1312020July291620100BBUSADirectDirect1021No DepositNo Agent0Transient135.0002No-Show
141494City Hotel1252020May18621100BBFRACorporateCorporate1010No DepositNo Agent0Transient125.0000No-Show
141495City Hotel162020July291710100BBPRTCorporateCorporate1110No DepositNo Agent0Transient65.0000No-Show
141496City Hotel102020August31202100BBPRTCorporateCorporate1000No DepositNo Agent0Transient65.0001No-Show

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledbooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_status# duplicates
6578City Hotel12772019November46712200BBPRTGroupsTA/TO0000Non RefundNo Agent0Transient100.000Canceled180
5193City Hotel1682019February81702200BBPRTGroupsTA/TO0100Non Refund370Transient75.000Canceled150
4799City Hotel1342018December50802100BBPRTOffline TA/TOTA/TO0100Non Refund190Transient90.000Canceled140
4803City Hotel1342019December50802100BBPRTOffline TA/TOTA/TO0100Non Refund190Transient90.000Canceled140
6200City Hotel11882019June251502100BBPRTOffline TA/TOTA/TO0000Non Refund11939Transient130.000Canceled109
5996City Hotel11582019May222402100BBPRTGroupsTA/TO0000Non Refund3731Transient130.000Canceled101
4730City Hotel1282020March9203200BBPRTGroupsTA/TO0000Non RefundNo Agent0Transient95.000Canceled99
4868City Hotel1382020January21401100BBPRTCorporateCorporate0000Non RefundNo Agent0Transient75.000Canceled99
5989City Hotel11562020April172603200BBPRTGroupsTA/TO0000Non Refund370Transient100.000Canceled99
5223City Hotel1712019June251403100BBPRTOffline TA/TOTA/TO0000Non Refund2360Transient120.000Canceled89